BackgroundDaily mortality is an important determinant of a vector's ability to transmit pathogens. Original simplifying assumptions in malaria transmission models presume vector mortality is independent of age, infection status and parasite load. Previous studies illustrate conflicting evidence as to the importance of Plasmodium-induced vector mortality, but very few studies to date have considered the effect of infection density on mosquito survival.MethodsA series of three experiments were conducted, each consisting of four cages of 400-1,000 Anopheles stephensi mosquitoes fed on blood infected with different Plasmodium berghei ookinete densities per microlitre of blood. Twice daily the numbers of dead mosquitoes in each group were recorded, and on alternate days a sample of live mosquitoes from each group were dissected to determine parasite density in both midgut and salivary glands.ResultsSurvival analyses indicate that mosquito mortality is both age- and infection intensity-dependent. Mosquitoes experienced an initially high, partly feeding-associated, mortality rate, which declined to a minimum before increasing with mosquito age and parasite intake. As a result, the life expectancy of a mosquito is shown to be dependent on both insect age and the density of Plasmodium infection.ConclusionThese results contribute to understanding in greater detail the processes that influence sporogony in the mosquito, indicate the impact that parasite density could have on malaria transmission dynamics, and have implications for the design, development, and evaluation of transmission-blocking strategies.

The impact of parasite density on malaria transmission remains unclear. We investigated sporogony temporal dynamics and the effect of parasite density on these dynamics. A series of experiments was conducted in which cages of mosquitoes were fed on blood containing a range of ookinete densities. Samples of surviving mosquitoes were dissected over time post-feeding to count oocyst and sporozoite numbers. Results reveal a humped (convex) pattern of oocyst numbers and suggest that transition rates between sporogony stages are density dependent. This has implications for the design of parasite-mosquito interface studies and the development of transmission-blocking strategies.

In order to provide more efficient and reliable power services than the traditional grid, it is necessary for the smart grid to accurately predict the electric load. Recently, recurrent neural networks (RNNs) have attracted increasing attention in this task because it can discover the temporal correlation between current load data and those long-ago through the self-connection of the hidden layer. Unfortunately, the traditional RNN is prone to the vanishing or exploding gradient problem with the increase of memory depth, which leads to the degradation of predictive accuracy. Many RNN architectures address this problem at the expense of complex internal structures and increased network parameters. Motivated by this, this article proposes two adaptive modularized RNNs to tackle the challenge, which can not only solve the gradient problem effectively with a simple architecture, but also achieve better performance with fewer parameters than other popular RNNs.

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